Prediction Model Selection and Performance Evaluation in Multiple Imputed Datasets

Provides functions to apply pooling or backward selection for logistic or Cox regression prediction models in multiple imputed datasets. Backward selection can be done from the pooled model using Rubin's Rules (RR), the total covariance matrix (D1 method), pooling chi-square values (D2 method), pooling likelihood ratio statistics (D3) or pooling the median p-values. The model can contain continuous, dichotomous, categorical predictors and interaction terms between all type of these predictors. Continuous predictors can also be introduced as restricted cubic spline coefficients. It is also possible to force (spline) predictors or interaction terms in the model during predictor selection. The package also contains functions to generate apparent model performance measures over imputed datasets as ROC/AUC, R-squares, fit test values and calibration plots. A wrapper function over Frank Harrell's validate function is used for that. Bootstrap internal validation is performed in each imputed dataset and results are pooled. Backward selection as part of internal validation is optional and recommended. Also a function to externally validate logistic prediction models in multiple imputed datasets is available. Eekhout (2017) . Wiel (2009) . Marshall (2009) .


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install.packages("psfmi")

0.1.0 by Martijn Heymans, 4 months ago


https://github.com/mwheymans/psfmi


Report a bug at https://github.com/mwheymans/psfmi/issues


Browse source code at https://github.com/cran/psfmi


Authors: Martijn Heymans [cre, aut] , Iris Eekhout [ctb]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports survival, car, norm, miceadds, mitools, foreign, pROC, rms, ResourceSelection, ggplot2

Suggests knitr, rmarkdown, testthat


See at CRAN